From 4e37a5fda20f0878b593b8ba2b9ea46db63743b5 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期四, 11 五月 2023 14:16:28 +0800
Subject: [PATCH] update repo
---
egs/librispeech_100h/conformer/run.sh | 112 +++++++++++++------------------------------------------
1 files changed, 27 insertions(+), 85 deletions(-)
diff --git a/egs/librispeech_100h/conformer/run.sh b/egs/librispeech_100h/conformer/run.sh
index 93d1b46..354610c 100755
--- a/egs/librispeech_100h/conformer/run.sh
+++ b/egs/librispeech_100h/conformer/run.sh
@@ -3,8 +3,8 @@
. ./path.sh || exit 1;
# machines configuration
-CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
-gpu_num=8
+CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=2
count=1
gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
@@ -16,30 +16,26 @@
feats_dir="../DATA" #feature output dictionary
exp_dir="."
lang=en
-dumpdir=dump/fbank
-feats_type=fbank
token_type=bpe
-dataset_type=large
-scp=feats.scp
-type=kaldi_ark
-stage=3
-stop_stage=4
+type=sound
+scp=wav.scp
+stage=1
+stop_stage=1
# feature configuration
feats_dim=80
-sample_frequency=16000
-nj=100
-speed_perturb="0.9,1.0,1.1"
+nj=64
# data
-data_librispeech=
+raw_data=
+data_url=www.openslr.org/resources/12
# bpe model
nbpe=5000
bpemode=unigram
# exp tag
-tag=""
+tag="exp1"
. utils/parse_options.sh || exit 1;
@@ -49,13 +45,12 @@
set -u
set -o pipefail
-train_set=train_960
+train_set=train_clean_100
valid_set=dev
test_sets="test_clean test_other dev_clean dev_other"
asr_config=conf/train_asr_conformer.yaml
-#asr_config=conf/train_asr_conformer_uttnorm.yaml
-model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
+model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer.yaml
#inference_config=conf/decode_asr_transformer_beam60_ctc0.3.yaml
@@ -73,69 +68,25 @@
_ngpu=0
fi
-if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
- echo "stage 0: Data preparation"
- # Data preparation
- for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
- local/data_prep_librispeech.sh ${data_librispeech}/LibriSpeech/${x} ${feats_dir}/data/${x//-/_}
+
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+ echo "stage -1: Data Download"
+ for part in dev-clean test-clean dev-other test-other train-clean-100; do
+ local/download_and_untar.sh ${raw_data} ${data_url} ${part}
done
fi
-feat_train_dir=${feats_dir}/${dumpdir}/$train_set; mkdir -p ${feat_train_dir}
-feat_dev_clean_dir=${feats_dir}/${dumpdir}/dev_clean; mkdir -p ${feat_dev_clean_dir}
-feat_dev_other_dir=${feats_dir}/${dumpdir}/dev_other; mkdir -p ${feat_dev_other_dir}
-feat_test_clean_dir=${feats_dir}/${dumpdir}/test_clean; mkdir -p ${feat_test_clean_dir}
-feat_test_other_dir=${feats_dir}/${dumpdir}/test_other; mkdir -p ${feat_test_other_dir}
-feat_dev_dir=${feats_dir}/${dumpdir}/$valid_set; mkdir -p ${feat_dev_dir}
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+ echo "stage 0: Data preparation"
+ # Data preparation
+ for x in dev-clean dev-other test-clean test-other train-clean-100; do
+ local/data_prep.sh ${raw_data}/LibriSpeech/${x} ${feats_dir}/data/${x//-/_}
+ done
+fi
+
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
- echo "stage 1: Feature Generation"
- # compute fbank features
- fbankdir=${feats_dir}/fbank
- for x in dev_clean dev_other test_clean test_other; do
- utils/compute_fbank.sh --cmd "$train_cmd" --nj 1 --max_lengths 3000 --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \
- ${feats_dir}/data/${x} ${exp_dir}/exp/make_fbank/${x} ${fbankdir}/${x}
- utils/fix_data_feat.sh ${fbankdir}/${x}
- done
-
- mkdir ${feats_dir}/data/$train_set
- train_sets="train_clean_100 train_clean_360 train_other_500"
- for file in wav.scp text; do
- ( for f in $train_sets; do cat $feats_dir/data/$f/$file; done ) | sort -k1 > $feats_dir/data/$train_set/$file || exit 1;
- done
- utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --max_lengths 3000 --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} --speed_perturb ${speed_perturb} \
- ${feats_dir}/data/$train_set ${exp_dir}/exp/make_fbank/$train_set ${fbankdir}/$train_set
- utils/fix_data_feat.sh ${fbankdir}/$train_set
-
- # compute global cmvn
- utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} \
- ${fbankdir}/$train_set ${exp_dir}/exp/make_fbank/$train_set
-
- # apply cmvn
- utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
- ${fbankdir}/$train_set ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/$train_set ${feat_train_dir}
- utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
- ${fbankdir}/dev_clean ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/dev_clean ${feat_dev_clean_dir}
- utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1\
- ${fbankdir}/dev_other ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/dev_other ${feat_dev_other_dir}
- utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
- ${fbankdir}/test_clean ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/test_clean ${feat_test_clean_dir}
- utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
- ${fbankdir}/test_other ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/test_other ${feat_test_other_dir}
-
- cp ${fbankdir}/$train_set/text ${fbankdir}/$train_set/speech_shape ${fbankdir}/$train_set/text_shape ${feat_train_dir}
- cp ${fbankdir}/dev_clean/text ${fbankdir}/dev_clean/speech_shape ${fbankdir}/dev_clean/text_shape ${feat_dev_clean_dir}
- cp ${fbankdir}/dev_other/text ${fbankdir}/dev_other/speech_shape ${fbankdir}/dev_other/text_shape ${feat_dev_other_dir}
- cp ${fbankdir}/test_clean/text ${fbankdir}/test_clean/speech_shape ${fbankdir}/test_clean/text_shape ${feat_test_clean_dir}
- cp ${fbankdir}/test_other/text ${fbankdir}/test_other/speech_shape ${fbankdir}/test_other/text_shape ${feat_test_other_dir}
-
- dev_sets="dev_clean dev_other"
- for file in feats.scp text speech_shape text_shape; do
- ( for f in $dev_sets; do cat $feats_dir/${dumpdir}/$f/$file; done ) | sort -k1 > $feat_dev_dir/$file || exit 1;
- done
-
- #generate ark list
- utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_train_dir} ${fbankdir}/${train_set} ${feat_train_dir}
- utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_dev_dir} ${fbankdir}/${valid_set} ${feat_dev_dir}
+ echo "stage 1: Feature and CMVN Generation"
+ utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
fi
dict=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
@@ -152,15 +103,6 @@
spm_train --input=${feats_dir}/data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000
spm_encode --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${dict}
echo "<unk>" >> ${dict}
- wc -l ${dict}
-
- vocab_size=$(cat ${dict} | wc -l)
- awk -v v=,${vocab_size} '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.char
- awk -v v=,${vocab_size} '{print $0v}' ${feat_dev_dir}/text_shape > ${feat_dev_dir}/text_shape.char
- mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/$train_set
- mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/$valid_set
- cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/$train_set
- cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/$valid_set
fi
--
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